1 citations · 1 across the 9 of their papers we have counts for
10 papers
Spectral partitioning for -block averaging kernels of finite Markov chains
Michael C. H. Choi, Youjia Wang
We develop spectral algorithms for selecting state-space partitions that define averaging kernels for finite, ergodic and reversible Markov chains. For a partition , th…
On additive averaging kernels for finite Markov chains
Ryan J. Y. Lim, Michael C. H. Choi
We study additive mixtures of Markov kernels of the form , where , is a baseline sampler and is a Gibbs kernel induced by a partition of the s…
Optimising two-block averaging kernels to speed up Markov chains
Ryan J. Y. Lim, Michael C. H. Choi
We study the problem of selecting optimal two-block partitions to accelerate the mixing of finite Markov chains under group-averaging transformations. The main objectives considere…
Group-averaged Markov chains II: tuning of group action in finite state space
Michael C. H. Choi, Ryan J. Y. Lim, Youjia Wang
We study group-averaged Markov chains obtained by augmenting a -stationary kernel with orbit kernels induced by a group action. We analyse the Gibbs (), Metropolis--Hasti…
Group-averaged Markov chains: mixing improvement
Michael C. H. Choi, Youjia Wang
For Markov kernels on a general state space , we introduce a new class of averaged Markov kernels of induced by a group that acts on $\mathca…
Improving the convergence of Markov chains via permutations and projections
Michael C. H. Choi, Max Hird, Youjia Wang
This paper aims at improving the convergence to equilibrium of finite ergodic Markov chains via permutations and projections. First, we prove that a specific mixture of permuted Ma…
Information-theoretic classification of the cutoff phenomenon in Markov processes
Youjia Wang, Michael C. H. Choi
We investigate the cutoff phenomenon for Markov processes under information divergences such as -divergences and Rényi divergences. We classify most common divergences into four…
Geometry and factorization of multivariate Markov chains with applications to MCMC acceleration and approximate inference
Michael C. H. Choi, Youjia Wang, Geoffrey Wolfer
This paper analyzes the factorizability and geometry of transition matrices of multivariate Markov chains. Specifically, we demonstrate that the induced chains on factors of a prod…
Information divergences of Markov chains and their applications
Youjia Wang, Michael C. H. Choi
In this paper, we first introduce and define several new information divergences in the space of transition matrices of finite Markov chains which measure the discrepancy between t…
Markov chain entropy games and the geometry of their Nash equilibria
Michael C. H. Choi, Geoffrey Wolfer
We introduce and study a two-player zero-sum game between a probabilist and Nature defined by a convex function , a finite collection of Markov generators (or its…